A vestige of a prebiotic bonding machine is functioning within the contemporary ribosome.
Ontology highlight
ABSTRACT:
SUBMITTER: Krupkin M
PROVIDER: S-EPMC3158926 | biostudies-literature | 2011 Oct
REPOSITORIES: biostudies-literature
Publications
A vestige of a prebiotic bonding machine is functioning within the contemporary ribosome.
Krupkin Miri M Matzov Donna D Tang Hua H Metz Markus M Kalaora Rinat R Belousoff Matthew J MJ Zimmerman Ella E Bashan Anat A Yonath Ada A
Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20111001 1580
Based on the presumed capability of a prebiotic pocket-like entity to accommodate substrates whose stereochemistry enables the creation of chemical bonds, it is suggested that a universal symmetrical region identified within all contemporary ribosomes originated from an entity that we term the 'proto-ribosome'. This 'proto-ribosome' could have evolved from an earlier machine that was capable of performing essential tasks in the RNA world, called here the 'pre-proto-ribosome', which was adapted f ...[more]
Similar Datasets
Project description:An indispensable prerequisite for establishing a scenario of life emerging by natural processes is the requirement that the first simple proto-molecules could have had a realistic probability of self-assembly from random molecular polymers in the prebiotic world. The vestige of the proto-ribosome, which is believed to be still embedded in the contemporary ribosome, is used to assess the feasibility of such spontaneous emergence. Three concentric structural elements of different magnitudes, having a dimeric nature derived from the symmetrical region of the ribosomal large subunit, were suggested to constitute the vestige of the proto-ribosome. It is assumed to have materialized spontaneously in the prebiotic world, catalyzing non-coded peptide bond formation and simple elongation. Probabilistic and energetic considerations are applied in order to evaluate the suitability of the three contenders for being the initial proto-ribosome. The analysis points to the simplest proto-ribosome, comprised of a dimer of tRNA-like molecules presently embedded in the core of the symmetrical region, as the only one having a realistic statistical likelihood of spontaneous emergence from random RNA chains. Hence it offers a feasible starting point for a continuous evolutionary path from the prebiotic matter, through natural processes, into the intricate modern translation system.
| S-EPMC6274258 | biostudies-literature
Project description:As halogen bonds gain prevalence in supramolecular synthesis and materials chemistry, it has become necessary to examine more closely how such interactions compete with or complement hydrogen bonds whenever both are present within the same system. As hydrogen and halogen bonds have several fundamental features in common, it is often difficult to predict which will be the primary interaction in a supramolecular system, especially as they have comparable strength and geometric requirements. To address this challenge, a series of molecules containing both hydrogen- and halogen-bond donors were co-crystallized with various monotopic, ditopic symmetric and ditopic asymmetric acceptor molecules. The outcome of each reaction was examined using IR spectroscopy and, whenever possible, single-crystal X-ray diffraction. 24 crystal structures were obtained and subsequently analyzed, and the synthon preferences of the competing hydrogen- and halogen-bond donors were rationalized against a background of calculated molecular electrostatic potential values. It has been shown that readily accessible electrostatic potentials can offer useful practical guidelines for predicting the most likely primary synthons in these co-crystals as long as the potential differences are weighted appropriately.
| S-EPMC4547818 | biostudies-literature
Project description:ObjectiveTo evaluate multidisciplinary female representation at urologic oncology conferences, we reviewed speakership trends at contemporary Society of Urologic Oncology (SUO) and American Society of Clinical Oncology Genitourinary Symposium (GU-ASCO) annual meetings.MethodsMeeting programs from SUO and GU-ASCO from 2015 to 2019 were analyzed. Biographical information was determined by querying institutional websites and social/professional media platforms. Statistical analyses were performed to assess for differences and relationships between male and female authorship based on gender, specialty, professional, and educational factors.ResultsWe identified 1102 speakers at genitourinary oncology conferences. Overall, 222 (20%) were female. There was no significant difference between female speakership rates at SUO and GU-ASCO. The overall proportion of female speakers increased over time, but not when analyzing each individual subspecialty conference separately. Several professional and educational differences were noted between genders. Female speakers were more likely to be medical oncologists (P <.001), have more recent years of graduation (2001 vs 1996, P <.001), hold an additional advanced degree (OR 2.09, P = .005), and speak in sessions where other women served as chair (OR 1.42, P = .044). Conversely, female speakers had lower odds of delivering a plenary or keynote address (OR 0.28, P = .015).ConclusionWe identified a significantly positive trend towards increased female representation within contemporary genitourinary conferences over time; these trends did not remain significant when analyzing each meeting separately. Several important disparities between men and women speakers were identified. Our data suggests that inclusion of women in planning committees may help reduce gender bias and promote diversity within urologic oncology.
| S-EPMC9257999 | biostudies-literature
Project description:Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two limitations, high accuracy and abundance, are often taken together; however, insight into the dataset accuracy limitation of contemporary machine learning algorithms may yield insight into whether non-bench experimental sources of data may be used to generate useful machine learning models where there is a paucity of experimental data. We took highly accurate data across six kinase types, one GPCR, one polymerase, a human protease, and HIV protease, and intentionally introduced error at varying population proportions in the datasets for each target. With the generated error in the data, we explored how the retrospective accuracy of a Naïve Bayes Network, a Random Forest Model, and a Probabilistic Neural Network model decayed as a function of error. Additionally, we explored the ability of a training dataset with an error profile resembling that produced by the Free Energy Perturbation method (FEP+) to generate machine learning models with useful retrospective capabilities. The categorical error tolerance was quite high for a Naïve Bayes Network algorithm averaging 39% error in the training set required to lose predictivity on the test set. Additionally, a Random Forest tolerated a significant degree of categorical error introduced into the training set with an average error of 29% required to lose predictivity. However, we found the Probabilistic Neural Network algorithm did not tolerate as much categorical error requiring an average of 20% error to lose predictivity. Finally, we found that a Naïve Bayes Network and a Random Forest could both use datasets with an error profile resembling that of FEP+. This work demonstrates that computational methods of known error distribution like FEP+ may be useful in generating machine learning models not based on extensive and expensive in vitro-generated datasets.
| S-EPMC6601015 | biostudies-literature
Project description:The ribosome translates the genetic code into proteins in all domains of life. Its size and complexity demand long-range interactions that regulate ribosome function. These interactions are largely unknown. Here, we apply a global coevolution method, statistical coupling analysis (SCA), to identify coevolving residue networks (sectors) within the 23S ribosomal RNA (rRNA) of the large ribosomal subunit. As in proteins, SCA reveals a hierarchical organization of evolutionary constraints with near-independent groups of nucleotides forming physically contiguous networks within the three-dimensional structure. Using a quantitative, continuous-culture-with-deep-sequencing assay, we confirm that the top two SCA-predicted sectors contribute to ribosome function. These sectors map to distinct ribosome activities, and their origins trace to phylogenetic divergences across all domains of life. These findings provide a foundation to map ribosome allostery, explore ribosome biogenesis, and engineer ribosomes for new functions. Despite differences in chemical structure, protein and RNA enzymes appear to share a common internal logic of interaction and assembly.
| S-EPMC7443888 | biostudies-literature
Project description:From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through "molecular synapses", ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the "sensory-proteins" innervate the functional ribosomal sites, while the "inter-proteins" interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing.
| S-EPMC4881015 | biostudies-literature
Project description:The folding of many proteins can begin during biosynthesis on the ribosome and can be modulated by the ribosome itself. Such perturbations are generally believed to be mediated through interactions between the nascent chain and the ribosome surface, but despite recent progress in characterising interactions of unfolded states with the ribosome, and their impact on the initiation of co-translational folding, a complete quantitative analysis of interactions across both folded and unfolded states of a nascent chain has yet to be realised. Here we apply solution-state NMR spectroscopy to measure transverse proton relaxation rates for methyl groups in folded ribosome-nascent chain complexes of the FLN5 filamin domain. We observe substantial increases in relaxation rates for the nascent chain relative to the isolated domain, which can be related to changes in effective rotational correlation times using measurements of relaxation and cross-correlated relaxation in the isolated domain. Using this approach, we can identify interactions between the nascent chain and the ribosome surface, driven predominantly by electrostatics, and by measuring the change in these interactions as the subsequent FLN6 domain emerges, we may deduce their impact on the free energy landscapes associated with the co-translational folding process.
| S-EPMC8513902 | biostudies-literature
Project description:Objective: This study aimed to systematically assess the characteristics and risk of bias of previous studies that have investigated nonlinear machine learning algorithms for warfarin dose prediction. Methods: We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), and Wanfang Database up to March 2022. We assessed the general characteristics of the included studies with respect to the participants, predictors, model development, and model evaluation. The methodological quality of the studies was determined, and the risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: From a total of 8996 studies, 23 were assessed in this study, of which 23 (100%) were retrospective, and 11 studies focused on the Asian population. The most common demographic and clinical predictors were age (21/23, 91%), weight (17/23, 74%), height (12/23, 52%), and amiodarone combination (11/23, 48%), while CYP2C9 (14/23, 61%), VKORC1 (14/23, 61%), and CYP4F2 (5/23, 22%) were the most common genetic predictors. Of the included studies, the MAE ranged from 1.47 to 10.86 mg/week in model development studies, from 2.42 to 5.18 mg/week in model development with external validation (same data) studies, from 12.07 to 17.59 mg/week in model development with external validation (another data) studies, and from 4.40 to 4.84 mg/week in model external validation studies. All studies were evaluated as having a high risk of bias. Factors contributing to the risk of bias include inappropriate exclusion of participants (10/23, 43%), small sample size (15/23, 65%), poor handling of missing data (20/23, 87%), and incorrect method of selecting predictors (8/23, 35%). Conclusions: Most studies on nonlinear-machine-learning-based warfarin prediction models show poor methodological quality and have a high risk of bias. The analysis domain is the major contributor to the overall high risk of bias. External validity and model reproducibility are lacking in most studies. Future studies should focus on external validity, diminish risk of bias, and enhance real-world clinical relevance.
| S-EPMC9147332 | biostudies-literature
Project description:Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.
| S-EPMC9295160 | biostudies-literature
Project description:The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
| S-EPMC7937616 | biostudies-literature